Effects of forest types on leaf functional traits and their interrelationships of Pinus massoniana coniferous and broad‐leaved mixed forests in the subtropical mountain, Southeastern China

Leaf functional traits are widely used to detect and explain adaptations that enable plants to live under various environmental conditions. This study aims to determine the difference in leaf functional traits among four forest types of Pinus massoniana coniferous and broad-leaved mixed forests by leaf morphological, nutrients, and stoichiometric traits in the subtropical mountain, Southeastern China. Our study indicated that the evergreen conifer species of P. massoniana had higher leaf dry matter content (LDMC), leaf C content, C/N and C/P ratios, while the three deciduous broad-leaved species of L. formosana, Q. tissima, and P. strobilacea had higher specific leaf area (SLA), leaf N, leaf P nutrient contents, and N/P ratio in the three mixed forest types. The results showed that the species of P. massoniana has adapted to the nutrient-poor environment by increasing their leaf dry matter for higher construction costs thereby reducing water loss and reflects a resource conservation strategy. In contrast, the three species of L. formosana, Q. tissima, and P. strobilacea exhibited an optimized resource acquisition strategy rather than resource conservation strategy in the subtropical mountain of southeastern China. Regarding the four forest types, the three mixed forest types displayed increased plant leaf nutrient contents when compared to the pure P. massoniana forest, especially the P. massoniana-L. formosana mixed forest type (PLM). Overall, variation in leaf functional traits among different forest types may play an adaptive role in the successful survival of plants under diverse environments because leaf functional traits can lead to significant effects on leaf function, especially for their acquisition of nutrients and use of light. The results of this study are beneficial to reveal the changes in plant leaf functional traits at the regional scale, which will provide a foundation for predicting changes in leaf traits and adaptation in the future environment.